Welcome back to the podcast, friends—this is Ira, and what I want to do today is take you deep into the Production schema of AdventureWorks2022. If you're a data professional, you probably already know how busy this database gets, but if you're just tuning in, let me give you the high-level: AdventureWorks2022 isn't just a toy database, it's a real peace of enterprise data modeling, reflecting real-world production with some impressive detail. Now, the database has five major schemas—HumanResources, Person, Production, Purchasing, and Sales. But it's that Production schema, with its 25 tables, that really tells the manufacturing story. It’s got tables like BillOfMaterials, Product, WorkOrder, TransactionHistory, and so on, each feeding into that larger manufacturing pipeline.BillOfMaterials? That's your hierarchical component breakdown—think of parts and assemblies for everything from a simple bike pedal to the full bike. Product table, well, that's your storefront inventory and product master—504 rows, 25 columns, detailing everything you need to know for a product. WorkOrder manages the manufacturing processes—think scheduling, execution, tracking progress. And then you have TransactionHistory, capturing the lifecycle of inventory movements with, I kid you not, over 113,000 rows. It's all about logistics at scale.Why make a sample database this realistic? It's funny, I sometimes get asked if Microsoft might have just modeled this after a real client—I can’t say for sure, but I swear, the AdventureWorks schema is a dead ringer for a project I did with a major bicycle maker back in the late '90s. Every time I look at the Product to BillOfMaterials to WorkOrder relationship, I feel a bit of déjà vu. You see, data models in manufacturing haven't changed all that much because the pipelines themselves—forecasting, procurement, assembly—haven't changed. What changes is the tech. But the underlying tables? Still the same backbone.And maybe that’s why these schemas are so useful for professionals practicing profiling, data governance, or just getting their heads around how manufacturing data flows. Like we've talked about in previous episodes, metadata, cardinality, frequency stats—it gives us more than columns; it gives us business insight. All right, let's break down individual structures, because some of these columns…they have stories to tell.Okay, diving in—let’s talk patterns and unique values. Data people, you know this: it's not all about the number of rows or tables, it's often about how the data is distributed within those columns. Take something like the Product table’s ProductNumber and Name columns—each with exactly 504 unique values. Maybe that sounds mundane, but what it really means is you have a one-to-one mapping from row to product, allowing each SKU or item to be tracked from manufacturing through to sales. This sort of high cardinality is exactly what you want in an ERP system when traceability and compliance are on the line.Now, ListPrice—that column’s got 103 unique values. And patterns emerge when you profile. ListPrice is what you’d expect: a spread of prices, with 46 of them showing up just once, so, some outliers, but also clustering, probably by product line. BOMLevel from BillOfMaterials? Five unique values—0 through 4—but the vast majority are clustered at level 1 and 2. That's your bread-and-butter assembly structure, straightforward hierarchy. You see the same thing in columns like Size, Weight, or even Color and Class—relatively low cardinality, but the presence of a few outliers.Why does this matter to ETL or profiling? Well, distinct values—or cardinality—impact everything from join performance to how you design aggregates or indexes. High cardinality on something like ProductNumber makes it a good candidate for distribution keys in parallel systems or for primary keys across joins. Low cardinality columns, like BOMLevel—especially when they skew heavily toward a single value—can't serve that same purpose without bottlenecking queries.And the significance isn’t just academic. When you’re profiling a system—before you even run an ETL—understanding these distributions lets you spot anomalies, create cleaner business logic, and yes, shave hours off your batch windows. Remember in last week’s episode, when we talked about uncovering business stories from odd-looking distributions? Same skillset here. If all your WorkOrder StockedQtys are 1, but your OrderQty varies, you start asking why—possible process quirk, ETL bug, or business rule?So, patterns and uniqueness—the secret sauce behind scalable, understandable, trustworthy production data. And it’s the sort of stuff you have to get right before you ever trust an AI model to run on top of it. Cardinality isn’t just a stat—it’s an insight into what your business really cares about.All right, now for the fun part—let’s dig into how the data is actually distributed. If you profile TransactionHistory or WorkOrder, the first thing that’ll smack you in the face is the dominance of certain values. In TransactionHistory’s Quantity column, the value '1' appears a whopping 65,305 times, massively dwarfing all others. Same story in WorkOrder’s StockedQty—again, ‘1’ overwhelms everything else. But what does that mean? Well, if you’re not careful, “most common” can become “background noise,” obscuring those rare, perhaps business-critical, exceptions.I’ve done audits where teams filtered for the top value and…that’s it. But that’s a trap because those repeat, low-variance values often hide the stuff you actually care about—errors, real demand spikes, upstream process failures, and so on. High rates of NULLs, which you see, for example, in columns like EndDate in BillOfMaterials, show up in profile results as almost 2,500 out of 2,679 rows. That’s huge, and it tells a story—most components remain current, or maybe never expire. If you didn’t check, you’d guess wrong about the lifecycle of your assemblies.And then there are the true outliers—like StockedQty in WorkOrder, which has over a thousand unique values, but again, most are '1.' You start realizing what’s considered “normal” in manufacturing data—a thousand exceptions are buried under a tidal wave of sameness. I saw this time and again doing data profiling for supply chain audits: ten thousand WorkOrders prepped for shipping, but only a handful ever diverge from the standard quantity. Why? Often, it’s because business processes are so standardized that the data just falls in line. But every deviation—every out-of-range actual cost, every unexpected transaction date spike—is an opportunity for insight, or a red flag.These distribution patterns—frequency of values, rare outliers, NULL rates—are what make data governance and AI work possible. You’ve got to know the scale you’re dealing with before you address anomalies, whether that’s through machine learning or straight-up rule-based detection. When I look at these tables, I see not just numbers, but stories—bulk processes with rare disruptions, supply chains marching to the rhythm of ‘1’ until something interesting, or dangerous, happens.And that’s a wrap for today’s deep dive, folks. Keep these patterns in mind: most data hides its meaning in how the numbers flow. The next time we meet, we’ll look at how those same patterns feed into demand forecasting or AI-powered decision automation. Until then, keep profiling, and keep asking questions—even when the data’s boring, the story often isn’t.

Episodes

Inside AdventureWorks2022 Production Schema